obsidian-wiki is a framework that helps AI agents build and maintain an interconnected knowledge base from text-based material in an Obsidian vault. It is for people who want their agents to remember discoveries, connect related information, and answer questions with wiki-link citations. Catalogue add-ons provide the agent skills, instructions, agents, and configuration used to create and maintain these wikis.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Ar9av/obsidian-wiki --skill session-searchgit clone --depth 1 https://github.com/Ar9av/obsidian-wikiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ar9av/obsidian-wiki/session-search)<a href="https://agentmods.dev/skills/ar9av/obsidian-wiki/session-search"><img src="https://agentmods.dev/badge/skills/ar9av/obsidian-wiki/session-search.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 62 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00128 | $0.01016 |
| Opus 5 | $0.00064 | $0.00508 |
| Sonnet 5 | $0.00026 | $0.00203 |
| Haiku 4.5 | $0.00013 | $0.00102 |
Grade A, and why
session-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session Search
Answers "which of my past sessions was about X" and then pulls that session's context in.
Step 1: Check the graph exists and is fresh
obsidian-wiki sessions-query "<topic>" --json
If this exits 1 with "run sessions-build first", tell the user and offer /session-brain. If
graph.json is more than ~7 days old, mention it and offer a rebuild — but do not silently
rebuild, since that is a multi-second operation the user did not ask for.
Step 2: Rank
The scoring already combines four signals, so take the ordering as given rather than re-ranking:
- similarity — TF-IDF cosine against the session's text
- cluster lift — a session inside the best-matching topic scores higher even if its own words never matched. This is why a session that never said "telemetry" can still surface for it.
- bookmark boost — a human already flagged this session as worth keeping
- time decay — 90-day half-life, applied with a floor so an old exact match still outranks a fresh weak one
Useful filters: --project NAME, --cluster N, --since DATE, --top N.
Step 3: Present
Show the top ~5 as a compact table — title, project, date, topic, and the why string, which
already explains the match. Do not dump the raw JSON at the user.
Two things must be stated honestly rather than glossed over:
- Entries with
loadable: falseare history-only: the transcript has been pruned from disk and only the prompts survive. They are listed inunloadablewith a reason. Say the transcript is gone; do not imply it can be retrieved. - If everything relevant is unloadable, answer from the prompt text that is there and say that is all that remains.
Step 4: Load — hand off, do not reimplement
should_load holds at most 3 session ids worth opening, already filtered to ones with
transcripts.
Prefer an existing loader skill if the user has one installed:
claude-session-load— loads a Claude session by idbookmark-load— use when the hit is bookmarked
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 98 lines · 128 tokens per session scan A 4fd1c50437ed
session-search is a skill published in the GitHub repository Ar9av/obsidian-wiki (3,364 stars, last pushed yesterday), licensed MIT. It adds 128 tokens to every session and 1,016 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
knowledge-base-management
A lifecycle system for managing an Obsidian knowledge base, which is a folder of linked notes. It organizes raw material, AI-maintained wiki pages, and generated views into separate layers.
llm-wiki
Maintain a personal team knowledge base using the LLM Wiki pattern — incremental ingest, query, and lint operations on a layered wiki architecture.
llm-wiki
Build and maintain a persistent, interlinked Obsidian-compatible markdown wiki using Karpathy's LLM Wiki pattern. Extension-backed with auto-generated metadata, guardrails, and 14 custom tools (+3 opt-in agent-trajectory tools).
link-memory
Use after important user-approved decisions, when durable context should be proposed or reviewed, and for explicit Link memory lifecycle work: remember, recall, review, update, archive, restore, forget, or explain local memories through the CLI without requiring MCP.
link-retrieve
Use before answering work that may depend on user memory, project history, source-backed notes, or prior decisions; retrieve compact Link context through the CLI without loading the whole wiki or requiring MCP.
link-ingest
Use when raw files are present, source pages look stale, or a user asks to ingest notes into Link; refresh source-backed wiki pages, propose memories, and validate updates through the CLI without MCP.